Overview

Dataset statistics

Number of variables13
Number of observations79215
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.9 MiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

df_index is highly correlated with X_22High correlation
X_19 is highly correlated with X_20 and 2 other fieldsHigh correlation
X_20 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_21 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_22 is highly correlated with df_index and 3 other fieldsHigh correlation
X_31 is highly correlated with X_33High correlation
X_33 is highly correlated with X_31High correlation
df_index is highly correlated with X_22High correlation
X_19 is highly correlated with X_20 and 2 other fieldsHigh correlation
X_20 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_21 is highly correlated with X_19 and 2 other fieldsHigh correlation
X_22 is highly correlated with df_index and 3 other fieldsHigh correlation
X_30 is highly correlated with X_32High correlation
X_32 is highly correlated with X_30High correlation
X_19 is highly correlated with X_21High correlation
X_20 is highly correlated with X_21 and 1 other fieldsHigh correlation
X_21 is highly correlated with X_19 and 1 other fieldsHigh correlation
X_22 is highly correlated with X_20High correlation
df_index is highly correlated with X_19 and 3 other fieldsHigh correlation
X_19 is highly correlated with df_index and 3 other fieldsHigh correlation
X_20 is highly correlated with df_index and 3 other fieldsHigh correlation
X_21 is highly correlated with df_index and 3 other fieldsHigh correlation
X_22 is highly correlated with df_index and 3 other fieldsHigh correlation
df_index is uniformly distributed Uniform

Reproduction

Analysis started2022-08-07 05:50:57.643680
Analysis finished2022-08-07 05:51:24.768451
Duration27.12 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct39608
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19803.25
Minimum0
Maximum39607
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:24.866020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1980
Q19901.5
median19803
Q329705
95-th percentile37626.3
Maximum39607
Range39607
Interquartile range (IQR)19803.5

Descriptive statistics

Standard deviation11433.77256
Coefficient of variation (CV)0.5773684907
Kurtosis-1.199999999
Mean19803.25
Median Absolute Deviation (MAD)9902
Skewness1.6561712 × 10-9
Sum1568714449
Variance130731155.1
MonotonicityNot monotonic
2022-08-07T14:51:25.026097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
< 0.1%
264072
 
< 0.1%
264002
 
< 0.1%
264012
 
< 0.1%
264022
 
< 0.1%
264032
 
< 0.1%
264042
 
< 0.1%
264052
 
< 0.1%
264062
 
< 0.1%
264082
 
< 0.1%
Other values (39598)79195
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
12
< 0.1%
22
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
62
< 0.1%
72
< 0.1%
82
< 0.1%
92
< 0.1%
ValueCountFrequency (%)
396071
< 0.1%
396062
< 0.1%
396052
< 0.1%
396042
< 0.1%
396032
< 0.1%
396022
< 0.1%
396012
< 0.1%
396002
< 0.1%
395992
< 0.1%
395982
< 0.1%

X_19
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct84
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.239343432
Minimum2.86
Maximum3.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:25.211478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.86
5-th percentile3.09
Q13.16
median3.22
Q33.31
95-th percentile3.45
Maximum3.75
Range0.89
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.1102015904
Coefficient of variation (CV)0.03401973046
Kurtosis-0.2850142993
Mean3.239343432
Median Absolute Deviation (MAD)0.07
Skewness0.4890459561
Sum256604.59
Variance0.01214439054
MonotonicityNot monotonic
2022-08-07T14:51:25.359262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.193195
 
4.0%
3.183135
 
4.0%
3.163068
 
3.9%
3.173059
 
3.9%
3.22904
 
3.7%
3.222878
 
3.6%
3.212844
 
3.6%
3.132769
 
3.5%
3.152762
 
3.5%
3.142759
 
3.5%
Other values (74)49842
62.9%
ValueCountFrequency (%)
2.861
 
< 0.1%
2.893
 
< 0.1%
2.94
 
< 0.1%
2.915
 
< 0.1%
2.924
 
< 0.1%
2.939
 
< 0.1%
2.9410
 
< 0.1%
2.9511
 
< 0.1%
2.9619
< 0.1%
2.9742
0.1%
ValueCountFrequency (%)
3.751
 
< 0.1%
3.741
 
< 0.1%
3.721
 
< 0.1%
3.711
 
< 0.1%
3.691
 
< 0.1%
3.662
< 0.1%
3.651
 
< 0.1%
3.642
< 0.1%
3.634
< 0.1%
3.622
< 0.1%

X_20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct79
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.184119422
Minimum2.83
Maximum3.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:25.516468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.83
5-th percentile3.03
Q13.1
median3.18
Q33.27
95-th percentile3.35
Maximum3.67
Range0.84
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.1052249117
Coefficient of variation (CV)0.03304678556
Kurtosis-0.7258073828
Mean3.184119422
Median Absolute Deviation (MAD)0.08
Skewness0.07319835121
Sum252230.02
Variance0.01107228205
MonotonicityNot monotonic
2022-08-07T14:51:25.670666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.112888
 
3.6%
3.092738
 
3.5%
3.122699
 
3.4%
3.12643
 
3.3%
3.262559
 
3.2%
3.292513
 
3.2%
3.082449
 
3.1%
3.132417
 
3.1%
3.282369
 
3.0%
3.252361
 
3.0%
Other values (69)53579
67.6%
ValueCountFrequency (%)
2.838
 
< 0.1%
2.849
 
< 0.1%
2.8516
< 0.1%
2.868
 
< 0.1%
2.8718
< 0.1%
2.8815
 
< 0.1%
2.8929
< 0.1%
2.927
< 0.1%
2.9138
< 0.1%
2.9234
< 0.1%
ValueCountFrequency (%)
3.671
 
< 0.1%
3.622
< 0.1%
3.612
< 0.1%
3.592
< 0.1%
3.583
< 0.1%
3.572
< 0.1%
3.561
 
< 0.1%
3.552
< 0.1%
3.542
< 0.1%
3.534
< 0.1%

X_21
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct77
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.173294326
Minimum2.83
Maximum3.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:25.830420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.83
5-th percentile3.03
Q13.09
median3.16
Q33.24
95-th percentile3.37
Maximum3.68
Range0.85
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation0.1066429125
Coefficient of variation (CV)0.03360637293
Kurtosis-0.2776467029
Mean3.173294326
Median Absolute Deviation (MAD)0.07
Skewness0.5241003791
Sum251372.51
Variance0.01137271079
MonotonicityNot monotonic
2022-08-07T14:51:25.985444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.13514
 
4.4%
3.113513
 
4.4%
3.093479
 
4.4%
3.123400
 
4.3%
3.083180
 
4.0%
3.132925
 
3.7%
3.072857
 
3.6%
3.142723
 
3.4%
3.152666
 
3.4%
3.172659
 
3.4%
Other values (67)48299
61.0%
ValueCountFrequency (%)
2.834
 
< 0.1%
2.842
 
< 0.1%
2.864
 
< 0.1%
2.877
 
< 0.1%
2.8815
 
< 0.1%
2.8919
< 0.1%
2.919
< 0.1%
2.9130
< 0.1%
2.9238
< 0.1%
2.9341
0.1%
ValueCountFrequency (%)
3.681
 
< 0.1%
3.613
 
< 0.1%
3.585
 
< 0.1%
3.573
 
< 0.1%
3.568
 
< 0.1%
3.555
 
< 0.1%
3.5411
< 0.1%
3.539
< 0.1%
3.5212
< 0.1%
3.5121
< 0.1%

X_22
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.232379979
Minimum2.85
Maximum3.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:26.144976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.85
5-th percentile3.07
Q13.14
median3.23
Q33.32
95-th percentile3.4
Maximum3.82
Range0.97
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1086628494
Coefficient of variation (CV)0.03361697887
Kurtosis-0.6259001337
Mean3.232379979
Median Absolute Deviation (MAD)0.09
Skewness0.05365041662
Sum256052.98
Variance0.01180761485
MonotonicityNot monotonic
2022-08-07T14:51:26.299888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.132594
 
3.3%
3.32496
 
3.2%
3.192481
 
3.1%
3.182444
 
3.1%
3.312423
 
3.1%
3.122409
 
3.0%
3.142397
 
3.0%
3.212394
 
3.0%
3.162374
 
3.0%
3.222330
 
2.9%
Other values (80)54873
69.3%
ValueCountFrequency (%)
2.855
 
< 0.1%
2.864
 
< 0.1%
2.878
 
< 0.1%
2.889
 
< 0.1%
2.8912
< 0.1%
2.914
< 0.1%
2.9116
< 0.1%
2.9221
< 0.1%
2.9323
< 0.1%
2.9428
< 0.1%
ValueCountFrequency (%)
3.821
< 0.1%
3.81
< 0.1%
3.791
< 0.1%
3.781
< 0.1%
3.771
< 0.1%
3.751
< 0.1%
3.731
< 0.1%
3.711
< 0.1%
3.692
< 0.1%
3.662
< 0.1%

X_30
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.37883772
Minimum0.57
Maximum2.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:26.453294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile1.35
Q11.37
median1.37
Q31.38
95-th percentile1.41
Maximum2.11
Range1.54
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.03008779557
Coefficient of variation (CV)0.02182112886
Kurtosis179.89617
Mean1.37883772
Median Absolute Deviation (MAD)0.01
Skewness-3.208254837
Sum109224.63
Variance0.0009052754424
MonotonicityNot monotonic
2022-08-07T14:51:26.602784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.3723423
29.6%
1.3821775
27.5%
1.3611932
15.1%
1.3910318
13.0%
1.353963
 
5.0%
1.42738
 
3.5%
1.48917
 
1.2%
1.49782
 
1.0%
1.47757
 
1.0%
1.34621
 
0.8%
Other values (35)1989
 
2.5%
ValueCountFrequency (%)
0.5724
 
< 0.1%
1.211
 
< 0.1%
1.272
 
< 0.1%
1.285
 
< 0.1%
1.292
 
< 0.1%
1.37
 
< 0.1%
1.3116
 
< 0.1%
1.3226
 
< 0.1%
1.3366
 
0.1%
1.34621
0.8%
ValueCountFrequency (%)
2.111
< 0.1%
2.091
< 0.1%
2.031
< 0.1%
21
< 0.1%
1.991
< 0.1%
1.871
< 0.1%
1.781
< 0.1%
1.751
< 0.1%
1.681
< 0.1%
1.631
< 0.1%

X_31
Real number (ℝ≥0)

HIGH CORRELATION

Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.571119864
Minimum0.6
Maximum7.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:26.752026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.51
Q11.53
median1.55
Q31.6
95-th percentile1.7
Maximum7.89
Range7.29
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.07509934482
Coefficient of variation (CV)0.04779988246
Kurtosis1061.112532
Mean1.571119864
Median Absolute Deviation (MAD)0.02
Skewness14.14912024
Sum124456.26
Variance0.005639911593
MonotonicityNot monotonic
2022-08-07T14:51:27.100533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5412208
15.4%
1.5311765
14.9%
1.529055
11.4%
1.558228
10.4%
1.565340
 
6.7%
1.514235
 
5.3%
1.573253
 
4.1%
1.582135
 
2.7%
1.612074
 
2.6%
1.622064
 
2.6%
Other values (80)18858
23.8%
ValueCountFrequency (%)
0.629
 
< 0.1%
1.432
 
< 0.1%
1.441
 
< 0.1%
1.452
 
< 0.1%
1.465
 
< 0.1%
1.475
 
< 0.1%
1.4816
 
< 0.1%
1.4992
 
0.1%
1.5875
 
1.1%
1.514235
5.3%
ValueCountFrequency (%)
7.891
< 0.1%
7.211
< 0.1%
3.481
< 0.1%
3.111
< 0.1%
2.961
< 0.1%
2.81
< 0.1%
2.791
< 0.1%
2.762
< 0.1%
2.71
< 0.1%
2.671
< 0.1%

X_32
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.362823076
Minimum0.57
Maximum2.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:27.261562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.57
5-th percentile1.34
Q11.35
median1.36
Q31.37
95-th percentile1.38
Maximum2.45
Range1.88
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.02936587865
Coefficient of variation (CV)0.02154782903
Kurtosis266.5905596
Mean1.362823076
Median Absolute Deviation (MAD)0.01
Skewness-4.140794936
Sum107956.03
Variance0.0008623548289
MonotonicityNot monotonic
2022-08-07T14:51:27.411790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1.3627380
34.6%
1.3521542
27.2%
1.3716383
20.7%
1.345743
 
7.2%
1.383382
 
4.3%
1.461316
 
1.7%
1.47901
 
1.1%
1.45766
 
1.0%
1.33674
 
0.9%
1.39354
 
0.4%
Other values (35)774
 
1.0%
ValueCountFrequency (%)
0.5731
 
< 0.1%
1.261
 
< 0.1%
1.273
 
< 0.1%
1.285
 
< 0.1%
1.299
 
< 0.1%
1.312
 
< 0.1%
1.3113
 
< 0.1%
1.3280
 
0.1%
1.33674
 
0.9%
1.345743
7.2%
ValueCountFrequency (%)
2.451
< 0.1%
2.291
< 0.1%
2.141
< 0.1%
2.111
< 0.1%
1.961
< 0.1%
1.921
< 0.1%
1.91
< 0.1%
1.842
< 0.1%
1.771
< 0.1%
1.71
< 0.1%

X_33
Real number (ℝ≥0)

HIGH CORRELATION

Distinct120
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.596088115
Minimum0.61
Maximum8.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:27.574645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.61
5-th percentile1.53
Q11.55
median1.57
Q31.61
95-th percentile1.77
Maximum8.95
Range8.34
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.1192228405
Coefficient of variation (CV)0.07469690391
Kurtosis777.5093417
Mean1.596088115
Median Absolute Deviation (MAD)0.02
Skewness15.57564374
Sum126434.12
Variance0.0142140857
MonotonicityNot monotonic
2022-08-07T14:51:27.722211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5511704
14.8%
1.5610880
13.7%
1.578933
11.3%
1.547754
9.8%
1.586141
 
7.8%
1.534456
 
5.6%
1.593984
 
5.0%
1.62678
 
3.4%
1.522002
 
2.5%
1.611809
 
2.3%
Other values (110)18874
23.8%
ValueCountFrequency (%)
0.61156
 
0.2%
1.431
 
< 0.1%
1.485
 
< 0.1%
1.4915
 
< 0.1%
1.597
 
0.1%
1.51602
 
0.8%
1.522002
 
2.5%
1.534456
 
5.6%
1.547754
9.8%
1.5511704
14.8%
ValueCountFrequency (%)
8.951
< 0.1%
8.071
< 0.1%
7.861
< 0.1%
7.811
< 0.1%
7.61
< 0.1%
7.531
< 0.1%
6.541
< 0.1%
5.971
< 0.1%
5.761
< 0.1%
5.61
< 0.1%

X_34
Real number (ℝ≥0)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.95020678
Minimum12.84
Maximum13.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:27.858853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.84
5-th percentile12.87
Q112.92
median12.96
Q312.99
95-th percentile13.01
Maximum13.23
Range0.39
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.04412292821
Coefficient of variation (CV)0.003407121521
Kurtosis-0.6989091487
Mean12.95020678
Median Absolute Deviation (MAD)0.03
Skewness-0.4221656177
Sum1025850.63
Variance0.001946832794
MonotonicityNot monotonic
2022-08-07T14:51:27.989385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
12.9710031
12.7%
12.999820
12.4%
12.967879
9.9%
12.946078
 
7.7%
12.985410
 
6.8%
12.924756
 
6.0%
12.874377
 
5.5%
13.014207
 
5.3%
12.893960
 
5.0%
133869
 
4.9%
Other values (17)18828
23.8%
ValueCountFrequency (%)
12.8445
 
0.1%
12.85221
 
0.3%
12.862038
2.6%
12.874377
5.5%
12.882283
2.9%
12.893960
5.0%
12.91759
 
2.2%
12.913745
4.7%
12.924756
6.0%
12.932609
3.3%
ValueCountFrequency (%)
13.231
 
< 0.1%
13.091
 
< 0.1%
13.089
 
< 0.1%
13.077
 
< 0.1%
13.0634
 
< 0.1%
13.0555
 
0.1%
13.04339
 
0.4%
13.03945
 
1.2%
13.02995
 
1.3%
13.014207
5.3%

X_35
Real number (ℝ≥0)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.9204027
Minimum12.81
Maximum13.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:28.132171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.81
5-th percentile12.85
Q112.87
median12.92
Q312.97
95-th percentile13
Maximum13.09
Range0.28
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.0521395629
Coefficient of variation (CV)0.004035444104
Kurtosis-1.232695046
Mean12.9204027
Median Absolute Deviation (MAD)0.05
Skewness0.1062605863
Sum1023489.7
Variance0.00271853402
MonotonicityNot monotonic
2022-08-07T14:51:28.259763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
12.868153
 
10.3%
12.887673
 
9.7%
12.987429
 
9.4%
12.965802
 
7.3%
12.854833
 
6.1%
12.94664
 
5.9%
12.954595
 
5.8%
12.874407
 
5.6%
12.934365
 
5.5%
12.914226
 
5.3%
Other values (18)23068
29.1%
ValueCountFrequency (%)
12.8163
 
0.1%
12.82231
 
0.3%
12.831274
 
1.6%
12.841184
 
1.5%
12.854833
6.1%
12.868153
10.3%
12.874407
5.6%
12.887673
9.7%
12.892815
 
3.6%
12.94664
5.9%
ValueCountFrequency (%)
13.091
 
< 0.1%
13.077
 
< 0.1%
13.069
 
< 0.1%
13.0557
 
0.1%
13.0462
 
0.1%
13.03280
 
0.4%
13.02877
 
1.1%
13.01971
 
1.2%
133675
4.6%
12.993430
4.3%

X_36
Real number (ℝ≥0)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.94169147
Minimum12.84
Maximum13.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:28.387245image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.84
5-th percentile12.87
Q112.9
median12.95
Q312.98
95-th percentile13.01
Maximum13.09
Range0.25
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.04801667182
Coefficient of variation (CV)0.003710231535
Kurtosis-1.116694626
Mean12.94169147
Median Absolute Deviation (MAD)0.04
Skewness-0.1963443169
Sum1025176.09
Variance0.002305600772
MonotonicityNot monotonic
2022-08-07T14:51:28.519098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12.998549
10.8%
12.978530
10.8%
12.966895
 
8.7%
12.876655
 
8.4%
12.895784
 
7.3%
12.945333
 
6.7%
12.984643
 
5.9%
12.924381
 
5.5%
12.914031
 
5.1%
13.013838
 
4.8%
Other values (16)20576
26.0%
ValueCountFrequency (%)
12.84162
 
0.2%
12.85408
 
0.5%
12.863245
4.1%
12.876655
8.4%
12.883402
4.3%
12.895784
7.3%
12.92063
 
2.6%
12.914031
5.1%
12.924381
5.5%
12.932350
 
3.0%
ValueCountFrequency (%)
13.093
 
< 0.1%
13.085
 
< 0.1%
13.076
 
< 0.1%
13.0637
 
< 0.1%
13.0568
 
0.1%
13.04288
 
0.4%
13.03891
 
1.1%
13.02885
 
1.1%
13.013838
4.8%
133667
4.6%

X_37
Real number (ℝ≥0)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.91931642
Minimum12.81
Maximum13.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size619.0 KiB
2022-08-07T14:51:28.660550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12.81
5-th percentile12.85
Q112.87
median12.91
Q312.97
95-th percentile13
Maximum13.08
Range0.27
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.05231542438
Coefficient of variation (CV)0.004049395703
Kurtosis-1.231453122
Mean12.91931642
Median Absolute Deviation (MAD)0.05
Skewness0.1474668409
Sum1023403.65
Variance0.002736903628
MonotonicityNot monotonic
2022-08-07T14:51:28.785750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
12.868388
 
10.6%
12.888279
 
10.5%
12.987167
 
9.0%
12.965644
 
7.1%
12.854923
 
6.2%
12.94640
 
5.9%
12.874449
 
5.6%
12.954388
 
5.5%
12.934290
 
5.4%
12.914083
 
5.2%
Other values (18)22964
29.0%
ValueCountFrequency (%)
12.8134
 
< 0.1%
12.82270
 
0.3%
12.831312
 
1.7%
12.841209
 
1.5%
12.854923
6.2%
12.868388
10.6%
12.874449
5.6%
12.888279
10.5%
12.892881
 
3.6%
12.94640
5.9%
ValueCountFrequency (%)
13.081
 
< 0.1%
13.078
 
< 0.1%
13.067
 
< 0.1%
13.0547
 
0.1%
13.0456
 
0.1%
13.03331
 
0.4%
13.02930
 
1.2%
13.01870
 
1.1%
133753
4.7%
12.993348
4.2%

Interactions

2022-08-07T14:51:22.368543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:50:59.857037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:01.765341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:03.499391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:05.451292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:07.210013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:08.970409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:11.010549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:12.830763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:14.716111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:16.595277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:18.495765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:20.356652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:22.515625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:00.010487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:01.910181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:03.823077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:05.597584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:07.352251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:09.123035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:11.159372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:12.985971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:14.853624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:16.751721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:18.649370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:20.507255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:22.649576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:00.151116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:02.035525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:03.948561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:05.721910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:07.478204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:09.255685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:11.288861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:13.121067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:14.977205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:16.887023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:18.782019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:20.637911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:22.786014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:00.294310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:02.161133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:04.081572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:05.852222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:07.606303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:09.395185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:11.422199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:13.261355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:15.100560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:17.026178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:18.922648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:20.772556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:22.923538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:00.433573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:02.286729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:04.209427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:05.980484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:07.735630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:09.531963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:11.556397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:13.402638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:15.224372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:17.166521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:19.059825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:20.909196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:23.057798image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:00.572854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:02.413257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:04.339795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:06.110472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:07.864966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:09.852108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:11.691636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:13.540682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:15.350916image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:17.309691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:19.196465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:21.050822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:23.206728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:00.724719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:02.553854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:04.479470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:06.252517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:08.006421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:10.003580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:11.837871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:13.688778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:15.484073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:17.464087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:19.344630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:21.198433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:23.352966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:00.872567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:02.687594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:04.621167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:06.391441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:08.146626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:10.149641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:11.981045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:13.836934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:15.802569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:17.614508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:19.490246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:21.340174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:23.504500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:01.034065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:02.833650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:04.767167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:06.536799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:08.292167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:10.301312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:12.130996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:13.990737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:15.941753image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:17.769055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:19.644993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:21.487256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:23.634190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:01.169182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:02.956467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:04.891739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:06.661479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:08.417836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:10.432943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:12.259567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:14.124347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:16.058457image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:17.901665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:19.776836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:21.614630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:23.783688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:01.320760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:03.097691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:05.040570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:06.805240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:08.563452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:10.583026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:12.407570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:14.277699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:16.196540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:18.055504image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:19.925711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:21.949681image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:23.926962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:01.471072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:03.233228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:05.178918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:06.941843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:08.704081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:10.727983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:12.550646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:14.427281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:16.331715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:18.205187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:20.073459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:22.090180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:24.067370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:01.616714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:03.367644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:05.315962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:07.076108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:08.839751image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:10.870242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:12.691923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:14.571271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:16.462393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:18.352079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:20.214256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-08-07T14:51:22.230251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-08-07T14:51:28.916996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-07T14:51:29.134592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-07T14:51:29.324123image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-07T14:51:29.515588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-07T14:51:24.266060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-07T14:51:24.570038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexX_19X_20X_21X_22X_30X_31X_32X_33X_34X_35X_36X_37
003.113.173.063.131.491.691.461.7412.9912.8812.8912.99
112.973.112.913.201.491.671.451.6612.9212.8712.8912.93
223.043.043.013.121.491.691.461.6812.9712.8712.8713.00
333.053.013.023.081.471.681.471.6812.9112.9712.9912.92
443.043.073.003.121.491.681.471.8212.9612.8512.9112.96
553.223.203.163.221.501.651.481.6712.9612.9113.0112.99
663.243.113.203.201.461.771.471.9412.9512.8912.9412.86
773.253.083.203.181.471.721.482.0013.0112.8612.8712.88
883.123.183.133.111.501.641.461.6412.9713.0012.8612.88
993.003.093.033.081.511.681.481.9912.9812.8512.9412.97

Last rows

df_indexX_19X_20X_21X_22X_30X_31X_32X_33X_34X_35X_36X_37
79205395983.163.112.983.021.361.611.361.8112.9212.9512.9713.00
79206395993.113.033.073.061.401.541.381.6012.9513.0012.8612.96
79207396003.243.003.123.061.361.591.361.6012.9612.8612.9112.95
79208396013.153.053.073.101.381.671.361.6412.9812.9012.9412.88
79209396023.193.013.073.041.341.581.361.6612.9412.8613.0012.95
79210396033.163.063.073.091.371.661.361.5612.9813.0012.9112.90
79211396043.182.983.093.061.361.641.361.6812.9212.9512.9913.00
79212396053.183.023.093.071.401.621.351.7212.9912.8813.0112.85
79213396063.143.063.023.111.381.561.371.5912.9713.0012.9912.90
79214396073.153.083.073.151.381.541.361.6912.9712.9912.9912.86